Echocardiography workstation

ABSTRACT

A digital image processing system for enhancing the image quality and diagnostic capabilities of conventional medical diagnostic ultrasound imaging systems and, more particularly, to an echocardiography workstation ( 10 ) which provides speckle reduction ( 200 ), edge detection, color quantitation ( 306 ), automatic diagnostic features. a built-in Help system for echocardiography, automatic quantitative analysis of left ventricular function, tomographic perfusion display ( 36 ), 3-D analysis, and report generation for improved analysis of echocardiograms.

This application claims the benefit of provisional application Ser. No.60/079,972 filed Mar. 30, 1998.

FIELD OF THE INVENTION

This invention relates to a digital image processing system forenhancing the image quality and diagnostic capabilities of conventionalmedical diagnostic ultrasound imaging systems and, more particularly, toan echocardiography workstation which provides speckle reduction, edgedetection, color quantitation, automatic diagnostic features, a built-inHelp system for echocardiography, automatic quantitative analysis ofleft ventricular function, tomographic perfusion display, 3-D analysis,and report generation for improved analysis of echocardiograms.

BACKGROUND OF THE INVENTION

Diagnostic ultrasound applies high frequency pulsed and continuous soundwaves to the body and uses computer-assisted processing of the reflectedsound waves to develop images of internal organs and the vascularsystem. The waves are generated and recorded by transducers or probesthat are either passed over or inserted into the body. The resultingimages can be viewed immediately on a video display or can be recordedfor later evaluation by the physician in continuous or single imageformats.

Diagnostic ultrasound imaging is now the preferred imaging modality inradiology, cardiology, and obstetrics and gynecology. Cardiologists andother medical practitioners use cardiac ultrasound imaging, orechocardiography, to evaluate the condition of the heart.Echocardiography is quick, relatively inexpensive, convenient, safe, andnon-invasive, and can be performed in real-time in private offices aswell as hospitals. The primary drawback of echocardiography has been thedifficulty of acquiring good quality images in patients with pooracoustic windows. These patients are estimated to comprise 10-30 percentof the patient population. Moreover, speckle noise and poor resolutioncan compromise the clinical utility of images of any patient produced byeven the most sophisticated ultrasound scanners. With echocardiography,the difficulty of acquiring acceptable images is further compounded bythe fact that the region of interest, the heart, has complex motionpatterns.

As a result of poor image quality, up to 10 percent of all rest echostudies and up to 30 percent of all stress echo studies of patients arenon-diagnostic. The most important factor is the presence of specklenoise, produced by the random pattern of overlapping echos that resultsfrom the scattering of the reflected sound waves. This pattern degradescontrast resolution and reduces the ability of an observer todiscriminate tissue boundaries and subtle image variations. Techniquesfor reducing such speckle noise while preserving and enhancing theintegrity of the myocardial borders and other cardiac structures remainhighly desirable.

Conventional echocardiographic assessment of heart function requires thedelineation of the endocardial borders throughout the cardiac cycle. Inthe images produced by conventional scanners, these borders are oftenobscured by speckle, masking, blurring, low contrast and interpolation.In addition, discontinuities frequently appear in the echocardiographicimage due to poor lateral resolution, which is inherent to ultrasoundimaging because portions of the cardiac border are always locatedparallel to the illuminating sonic beam. Such border definitiondifficulties are accentuated when performing stress echo studies, makingit very difficult to track the endocardial contours. A robust edgedetection/contour tracking algorithm is desired that can be usedeffectively in both rest and stress echocardiography.

In addition, while there are accepted global and regional quantitativemeasures of cardiac function, there are currently no effective toolsthat provide automatic quantitation of segmental cardiac function byechocardiography; rather, there are only qualitative assessment ormanual quantitative methods which are time-consuming and subject toobserver error. Moreover, previous attempts at displaying cardiac wallmotion based on boundary detection systems have improperly delineatedthe endocardial border and blended other signals between the endocardiumand other heart structures, such as papillary muscles, chordae, ormitral valve tissue. It remains desirable to provide reproducibleautomatic quantitation of global indices and regional wall motion and todisplay the results in a readily understandable format, such as acolor-coded format, whereby the effect of therapy and the evolution ofdisease may be more readily understood.

Also, at present, there are no systems known to the inventors whichprovide the physician with automatic interpretation assistance forspecific echocardiograms and offer the physician various diagnosticpossibilities that are consistent with the available data. Conventionalechocardiography review and reporting systems are off-line computersystems which are equipped with graphical tools and data entry screensthat facilitate on-screen measurement of digitized images and thegeneration and archiving of reports. Such systems are not configured tocapture video images, to perform image enhancement, edge detection, and3-D image analysis, or to perform stress echo studies. Such systems arealso very expensive. A cost-effective off-line and/or on-line analysisand report generation system remains desirable.

Accordingly, it is desired to provide a user-friendly echocardiographyworkstation that improves image quality, provides automatic edgedetection, quantitates endocardial wall movement, corrects for cardiactranslation, calculates 3-D left ventricle volume, and assists thephysician with the interpretation of echocardiograms. The presentinvention has been designed to meet these needs in the art.

SUMMARY OF THE INVENTION

The present invention addresses the above-mentioned needs in the art byproviding an echocardiography workstation that combines video captureand quad screen display for rest and stress echocardiography, specklereduction, edge detection, and cardiac contour tracking, automaticdiagnostic interpretation assistance, a built-in reference source (HELP)to assist the physician and technologist with evaluatingechocardiograms, color quantitation, report generation, and automaticwall motion analysis in a single system. The system also includes anoptional 3-D feature which utilizes a spatial locating device to obtaintomographic slices along a reference plane which are used for 3-Dreconstruction. The workstation of the invention thus complementsconventional cardiac ultrasound scanners to enhance the image quality ofechocardiograms and to automate functions that have previously beenperformed manually, thereby saving physician time and reducing costs,while also improving the capabilities of the cardiac scanner.

The workstation of the invention can be used to digitize the videooutput of cardiac ultrasound scanners. The user can then apply noisereduction algorithms that not only reduce excessive noise but alsoenhance the definition of cardiac structures. The enhanced images arefurther processed by boundary detection algorithms to automaticallyidentify the endocardial border and to track its movement through thecardiac cycle. The resulting delineation of the cardiac wall motionallows the physician to more quickly and accurately evaluate heartfunction. The system corrects for cardiac translation and the extent ofcardiac function (motion) is reproducibly automatically quantitated anddisplayed in a color-coded format which simplifies the physician'sreview process. Also, during the physician's review process, expertsystem software assists the physician in the interpretation process bylisting the various diagnostic possibilities that are consistent withthe available data. A Help system assists the physician with theinterpretation of the data by providing descriptions of abnormalitieswith lists of their known causes.

The workstation of the invention may also be used with spatial locatorsthat register the position and orientation of two-dimensional ultrasoundimages in a three-dimensional spatial coordinate system. This featureenables the system to perform more accurate calculations of cardiacfunction. The workstation also provides tomographic analysis software topermit the display of myocardial perfusion data for use in conjunctionwith ultrasound contrast agents. The invention also includes an R-wavesynchronization feature to synchronize images of varying frame lengthsand heart rates.

Preferably, the physician interacts with the workstation through agraphical user interface or by voice commands to view images, selectalternative processing options, consult reference sources, generatereports from pull-down menus, and store, retrieve, and transmitdigitized images and reports.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other novel features and advantages of the inventionwill become more apparent and more readily appreciated by those skilledin the art after consideration of the following description inconjunction with the associated drawings, of which:

FIG. 1 illustrates a generalized block diagram of an echocardiographysystem in accordance with a currently preferred embodiment of theinvention.

FIGS. 2a-2 e together illustrate a currently preferred embodiment of analgorithm for determining the left ventricular boundary location in areceived echocardiogram for contour tracking and quantitative analysisof left ventricular function.

FIG. 3 illustrates a currently preferred embodiment of a non-linear grayscale transformation curve implemented in the speckle reductiontechnique of the invention.

FIG. 4 illustrates a currently preferred embodiment of an algorithm forthe color quantitation of endocardial wall motion in accordance with theinvention.

FIG. 5 illustrates a quad display of a captured echocardiogram raw dataimage, the speckle reduced image, the edge detected image, and the colorquantitation of the movement of the image during the heart cycle alongwith a patient information screen.

FIG. 6 illustrates an interpretation screen indicating the structuresfor selection for interpretation by the physician.

FIG. 7 illustrates a quad display of the raw image, the speckle reducedimage, the edge detected image, and the color quantitation of themovement of the image during the heart cycle along with theabnormalities identified based on the measurement data.

FIG. 8 illustrates the interpretation screen whereby the physician mayenter a diagnosis of an echo-cardiogram for the selected heart segment.

FIG. 9 illustrates a sample diagnosis “AutoDx” screen illustrating thepossible diagnoses identified by the expert system.

FIG. 10 illustrates a sample echocardiography report generated by thereport generator of the invention.

DETAILED DESCRIPTION OF THE PRESENTLY PREFERRED EMBODIMENTS

A preferred embodiment of the invention will now be described in detailwith reference to FIGS. 1-10. Those skilled in the art will appreciatethat the description given herein with respect to those figures is forexemplary purposes only and is not intended in any way to limit thescope of the invention. For example, those skilled in the art willappreciate that while the preferred embodiment of the invention relatesto the ultrasonic imaging and analysis of the heart that the inventionmay be readily adapted for the imaging and analysis of other internalorgans and structures. Furthermore, the invention described herein maybe applied to other imaging modalities including MRI, CAT scan,angiography, and others in which speckle noise reduction and image edgedetection may be desirable. All questions regarding the scope of theinvention may be resolved by referring to the appended claims.

As will be explained in more detail below, the echocardiographyworkstation of the invention provides for image enhancement, edgedetection, quantitation, and assistance in the interpretation ofechocardiograms and the generation of reports as well as a Help systemfor echocardiography. The echocardiography workstation is an integratedhardware/software system which is compatible with conventional cardiacultrasound machines and blends digital image processing functions withadministrative capabilities in an interactive system that dramaticallyimproves the productivity of mainstream cardiologists.

The echocardiography workstation of the invention can be used todigitize the video output of the cardiac ultrasound system. It thenapplies the noise reduction algorithms, which not only reduce excessivenoise, but also enhance the definition of cardiac structures. Theenhanced images may, if selected by the user, undergo further processingby boundary detection algorithms to automatically identify theendocardial border and to track its movement through the cardiac cycle.Indices of cardiac function are automatically calculated by quantitationsoftware, and the results are displayed in color-coded format forimmediate review by the physician or technician. An integrated expertsystem alerts the physician to various diagnostic possibilities that areconsistent with the available data, including patient history and datafrom earlier studies that are stored in the workstation database.

The physician interacts with the echocardiography workstation through agraphical user interface or by voice commands to view images, to selectalternative processing options, to consult reference sources, togenerate reports from pull-down menus, and to store, retrieve andtransmit digitized images and reports. Reports that conventionally mayrequire hours or even days to produce and transmit to referringphysicians can be completed and communicated electronically in a matterof seconds.

The methods and apparatus of the present invention, or certain aspectsor portions thereof, may take the form of program code (i.e.,instructions) embodied in tangible media, such as floppy diskettes,CD-ROMs, hard drives, or any other machine-readable storage medium,wherein, when the program code is loaded into and executed by a machine,such as a computer, the machine becomes an apparatus for practicing theinvention. The methods and apparatus of the present invention may alsobe embodied in the form of program code that is transmitted over sometransmission medium, such as over electrical wiring or cabling, throughfiber optics, or via any other form of transmission, wherein, when theprogram code is received and loaded into and executed by a machine, suchas a computer, the machine becomes an apparatus for practicing theinvention. When implemented on a general-purpose processor, the programcode combines with the processor to provide a unique apparatus thatoperates analogously to specific logic circuits.

FIG. 1 illustrates a generalized block diagram of an echocardiographyworkstation 10 in accordance with a currently preferred embodiment ofthe invention. As shown in FIG. 1, the workstation 10 receives a videosignal from an echocardiographic machine including video source 12 andan R-wave counter/timer 14 which receives an R-wave pulse directly orfrom a tone to pulse converter 16. As shown, a video digitizer 18digitizes the received video signal for further processing. In thepreferred embodiment, the workstation 10 also receives spatialinformation via a spatial locator interface 20 from a spatial locatordevice 22, such as a BIRD™ electromagnetic tracking system, whichmeasures the real-time position and orientation (six degrees of freedom)of one or more miniaturized sensors for correlating the ultrasound imagelocation with the patient's body. The workstation 10 also receivesdiagnostic information from an expert system database 24 for use inautomatically evaluating the received echocardiogram. As will beexplained in more detail below, the user interface 26 allows the user toselect the desired processing and display features. These inputs areprocessed by conventional computer elements including processor 28, RAM30, mass storage 32 (which may or may not include the expert systemdatabase 24), display driver 34, and display monitor 36. Generally, eachof these elements is connected by a PCI or ISA data bus 38 so as tocommunicate data in a conventional fashion. Processor 28 functions byprocessing the software for implementing the speckle reduction, edgedetection, color quantitation, report generation, and databasemanagement algorithms, and the like, of the type described herein.

In a preferred embodiment, the digitized video images from the videodigitizer 18 are stored in mass storage 32 and/or RAM 30 as a frameconsisting of four subframes (Quad frame representation). In otherwords, the memory is divided into N logical image frames of the height(H) and width (W) of the image to be displayed, and each logical frameis divided into four logical quadrants of size H/2 by W/2. This approachallows four concurrent viewing windows to be synchronized for display sothat different views of a region or live and digitized reference viewsmay be viewed concurrently. Preferably, the first frame of a storedimage sequence is also displayed in the main viewing area of the display36 as a miniaturized thumbnail icon for easy retrieval of thecorresponding image sequence.

The processes of image capture, synchronization of different imagesusing the R-wave, and generating the afore-mentioned quad screen imagedisplay will now be described.

Image Capture:

To capture echocardiogram images, the workstation 10 waits for nextR-wave pulse from R-wave counter/timer 14. Upon receipt of the R-wavepulse, the received video signals from video source 12 are digitized byvideo digitizer 18 at 30 frames per second. As noted above, each frameof video is stored into contiguous logical image frames or into apre-selected quadrant of contiguous logical image frames of the hostmemory 30 or 32, where different views may be shown in each quadrant.The R-wave counter/timer 14 is interrogated when each frame is capturedto determine if an R-wave pulse (event) occurred during the time of theframe capture. If so, the frame identifier is stored in a list of frameidentifiers containing frame identifiers of all frames with anassociated R-wave pulse (event). This process continues until either (a)a pre-selected number of R-wave pulses (cardiac cycles) have beencaptured, or (b) the host memory 30 or 32 is filled. Once image captureis complete, the image quadrants are synchronized (R-wavesynchronization) as described below.

During the performance of the ultrasound investigation, the techniciancan select key image sequences to digitize to RAM 30 and to store to thehard drive or mass storage 32. When performing a rest echo, a fullscreen (640×480) or quad screen is digitized. The images are digitizedusing R-wave triggering device 14 that senses the R-wave voltage or theaudio beep. A menu allows the user to choose to digitize systole,diastole or both. The user may chose the number of R-wave cycles tocapture. The acquired images can then be processed as described below.

R-wave Synchronization:

Since up to four image sequences are displayed concurrently inaccordance with the invention, and since each cardiac cycle may containa different number of frames than other cardiac cycles, and since thedisplayed cardiac cycles of each of the (up to) four simultaneouslydisplayed image sequences must contain the same number of frames, framesare added to the image sequences that contain fewer frames per cardiaccycle than the displayed sequence with the most frames per cardiac cycleas follows.

Target frame counts are computed for systolic and diastolic portions ofthe cardiac cycle, where the target systolic frames are {square rootover (3.6N)} and the target diastolic frames are N minus the targetsystolic frames, where N is the number of frames in the selected cardiaccycle of the sequence with the maximum number of frames in the cycle.Then, for each cardiac cycle of each image sequence, the number ofsystolic frames to add and the number of diastolic frames to add arecomputed as follows.

The number of systolic frames to add is the target systolic frame minus{square root over (3.6N)}, and the number of diastolic frames is thetarget diastolic frame minus (N- {square root over (3.6N)}), where N isthe number of frames in the cardiac cycle. The systolic frames are thenrepeated from frame {square root over (3.6N)} to frame ( {square rootover (3.6N)} minus the add systolic frame number), while the diastolicframes are repeated for all frames in the range {square root over(3.6N)}+1 to N by int(add diastolic frames/{square root over (3.6N)})times. All frames in the range M= {square root over (3.6N)}+[N− {squareroot over (3.6N)}−MOD(add diastolic frames)* {square root over(3.6N)})]/2 to M+MOD(add diastolic frames, {square root over (3.6N)})are repeated one time each. If any of the sequences contains fewercardiac cycles then the others, the last cardiac cycle is repeated therequired number of times.

Image Display:

At a rate of 30 frames per second, the sequential logical image frames(entire frames, not quads) are transferred from the host memory 30 or 32to the display driver 34 for display on display monitor 36. The displaystarts on the selected frame and resets to the first selected frameafter the last selected frame has been displayed, where the selectedframes are usually those frames contained in one or more cardiac cycles.A cardiac cycle begins with the frame during which an R-wave pulseoccurred to the frame just prior to the frame during which the nextR-wave pulse occurred. Slow motion is implemented by displaying eachframe N times before moving to the next frame, and fast motion isimplemented by skipping frames in the sequence during display.

Spatial Location

Preferably, the technician can utilize the spatial locator 22 to allowthe capture of specific orthogonal slices and the calculation of 3-Dventricular volumes for the rendering of a 3-D model of the beatingheart. For this purpose, a spatial locator 22 such as the Bird™ fromAscension Technologies is attached to an ultrasound transducer. Thisdevice allows tracking of the spatial location of the transducer using 6degrees of freedom information, whereby the images are acquired at “IN”degree intervals with the 6 degrees of freedom coordinates stored witheach image. Preferably, a reference image sequence so acquired isdigitized by video digitizer 18 and stored in host memory 30 or 32. For3-D imaging, all images are displayed on a 3-D background or 3-Dbackground with a translucent 3-D model of the heart overlaid onto the2-D slices to indicate the slice position as it relates to the 3-Dmodel. On the other hand, the reference image sequence may be displayedin one quadrant of the display 36, while the live video is displayed inanother quadrant of the display 36. Then, as the technician moves thetransducer, the coordinates from the spatial locator 22 cause the cursoron the reference image to move, showing the location of the orthogonalslice in the live image in relation to the reference image or inrelation to the 3-D model.

Once the image is digitized, synchronized, and displayed, the operatormay elect to perform various operations on the displayed images toextract relevant information both for visual enhancement andquantitative analysis. This process will now be described with respectto FIGS. 2-4.

As illustrated in FIG. 2a, the first step in the processing of thedisplayed images is to capture the image sequence as described above atstep 200. In a preferred embodiment of the invention, the image capturestep further includes the step of utilizing a speckle reductionalgorithm to acquire a speckle reduced image by performing specklereduction on the displayed images to increase the signal-to-noise ratioof the video and to enhance features of the echocardiogram image. In thepreferred embodiment of the invention, the speckle reducingtwo-dimensional filtering is a three step process.

First, a non-linear gray scale transformation is performed on each imagein a sequence. In particular, each of the original 256 gray scale imagelevels is mapped non-linearly to another gray scale image level which isselected to enhance image contrast of the individual images. Typically,each pixel of each image in the sequence is remapped according to apre-stored look-up table taken from a class of look-up tables that hasthe shape illustrated generally in FIG. 3.

Second, recursive averaging is applied to the image sequence. Inparticular, each pixel in two or three consecutive frames is combinedwith the pixel in the same position in the previous and/or subsequent(in time) frame as:

pixel(present frame)=persistence*pixel(presentframe)+(1−persistence)*pixel(previous frame),

where “persistence” is selected by the user. Typical values forpersistence are 0.3, 0.4, 0.5, 0.6 and 0.7.

Finally, a two-dimensional non-linear gray level morphology operation isperformed on each pixel of an individual image. This operation isdescribed as follows:

4 1 3 4 1 3 2 2 X 2 2 3 1 4 3 1 4

A square neighborhood of each pixel of the image is chosen. The abovediagram represents a 5 by 5 neighborhood of the central pixel marked x.The user is given the option of using a 3 by 3, 5 by 5 or 7 by 7neighborhood. On this neighborhood, four directions containing the pixelx are chosen. In this diagram, the four sets of points are labeled1,1,x,1,1; 2,2,x,2,2; 3,3,x,3,3; 4,4,x,4,4. Each of these sets containsthe central pixel x and four other points. For each of these sets, themaximum number of the set is found as well as the minimum of these fourmaximums. The minimum number of each set is also found as well as themaximum of these four minimums. Finally, the pixel x is replaced withthe average of these two numbers (the minimum of the maximums and themaximum of the minimums).

The above operation is performed for all the pixels in the image, withspecial processing for edge effects. The process is then iterated agiven number of times which is controlled by the user. Presently, theuser may select 1, 2, 3, 4, or 5 iterations.

Once the above filtering is applied to the video sequences and the imageis captured at step 200, it is determined by the user whether or not toproceed with further processing. If so, the user determines whether thedisplayed images have adequate quality. If so, the edge detection ofFIGS. 2a-2 e is performed; otherwise, the video signal may be processedto improve image quality prior to the edge detection processing. Theframe corresponding to end-diastole and the frame corresponding toend-systole may be automatically brought up, where the framecorresponding to end-systole is found as an offset from the diastolicframe as determined by the equation: systolic frameoffset=sqrt(3.6*number of frames in cardiac cycle) as set forth above.

As will be explained in detail below, using the R-wave detector 14, theend-diastolic frame is automatically displayed so that the user maytrace the diastolic border on this frame or select the center of theblood pool of the end-diastolic frame so that this frame isautomatically traced and so that the endocardial wall on the current andsubsequent frames may be automatically detected by performing the edgedetection process of FIGS. 2a-2 e.

FIGS. 2a-2 e together illustrate a currently preferred embodiment of analgorithm for edge detection in accordance with the invention. By way ofexample, the edge detection algorithm is described in connection withthe determination of the left ventricular boundary location in thereceived image for contour tracking and quantitative analysis of theleft ventricular function.

Once the digitized image sequence from video digitizer 18 is capturedand the above-mentioned speckle reduction performed at step 200, the enddiastolic (ED) frames are tagged using the ECG R-wave at step 202. Theend systolic (ES) frames are also tagged at step 204 preferably using anon-linear prediction model based on the patient's heart rate. Thesystem is then calibrated at step 206, as necessary.

At step 208, the reviewer selects the cardiac cycle to be analyzed andtraces the ED border at step 210 or selects the center of the blood poolof the ED frame and the ED border is automatically traced. Thresholdsare then computed from the ED frame at step 212 preferably using thealgorithm described below with respect to FIG. 2b. Pre-edit boundariesfor all frames of the cardiac cycle are then detected at step 214preferably using the algorithm described below with respect to FIGS.2c-2 e. The ED and ES borders may then be manually edited at step 216before repeating the boundary detection algorithm of FIG. 2c fordetection of the post-edit boundaries for all frames of the cardiaccycle at step 218. As set forth in FIG. 2e, the edited points mayincorporate the Doppler predicted boundary points to fill in gaps in theedge data. Performing this process for each frame allows the physicianor technician to track the heart's contours during the diagnosticevaluation.

FIG. 2b illustrates in more detail a preferred embodiment of thethreshold computation algorithm implemented in step 212. As illustrated,the ED frame is accessed at step 220, and N rays are projected which arespaced at equal angles from the centroid of the drawn ED border(determined at step 210) outward at step 222. At step 224, the imagegray-level gradients along each ray in the vicinity of the drawn borderare found, and, if a gradient larger than a preset threshold is foundalong a ray, the border point is replaced with the gradient point atstep 226. A histogram is then computed at step 228.

Next, for each of M threshold values from 0 to a maximum gray level(step 230), the threshold value is histogram equalized (step 232), theED image is thresholded using the histogram equalized threshold value(step 234), and morphological opening and closing is performed (step236). Also, starting at the existing ED border, a point is found on eachray where the thresholded image changes state (step 238). Then, for eachray, an error distance between the located state-change point found instep 238 and the existing border is calculated at step 240. Once thisprocess has been performed for each of the M threshold values,processing proceeds to step 242.

At step 242, the left ventricle is divided into 8 pie wedge-shapedoctants. The threshold for each octant that corresponds to the smallestaverage error distance for that octant is then saved at step 244. Theimage is then thresholded at step 246 using the previously selectedthreshold value or next threshold value greater than the previouslyselected threshold for each octant. The average error distance for eachoctant is then computed at step 248 using the new threshold. Finally, atstep 250, for each octant, the new threshold value is saved if all ofthe new threshold points are outside of the existing border or the newerror distance is less than the older error distance multiplied by someweighting factor. Otherwise, the previous threshold value is saved.

FIG. 2c illustrates in more detail a preferred embodiment of theboundary detection algorithm implemented in steps 214 and 218. Asillustrated in FIG. 2a, the process repeats steps 252-282 twice for eachframe in the cardiac cycle, once prior to editing (step 214) and onceafter editing (step 218). In each loop, the threshold values for eachoctant are histogram equalized at step 254, and the image is thresholdedat step 256 using the histogram equalized threshold values for eachoctant. Morphological opening and closing is performed at step 258, andout-of-tolerance boundary points are invalidated at step 260, preferablyusing the algorithm of FIG. 2d. Isolated border points are invalidatedat step 262, and the boundary points are then low pass filtered at step264. The filtered border points are then interpolated at step 266 ingaps less than a predetermined number of rays wide based on the borderpoints on rays on either side of the gap.

At step 268, the image gray-level gradients along each ray in thevicinity of the border of the previous frame are found for invalidborder points, and the located gradient points are invalidated at step270 if the border point on the same ray of the previous frame was alsodetermined using the gradient. Out-of-tolerance boundary points foundusing the gradients are then invalidated at step 272, preferably usingthe algorithm of FIG. 2d. Once again, isolated border points areinvalidated at step 274, and out-of-tolerance boundary points using“final” tolerances are invalidated at step 276. Border points are theninterpolated at step 278 in gaps less than a predetermined number ofrays wide based on the border points on rays on either side of the gap.At step 280, pre-edit border gaps are filled from the border of theprevious frame, preferably using the algorithm of FIG. 2e, whilepost-edit border gaps may be filled with Doppler predicted border pointsas also illustrated in FIG. 2e. Finally, in the case of pre-edit frames,invalid border points are replaced at step 282 with border points takenfrom the previous frame and corrected for translation of the leftventricle (LV) centroid from the previous frame to the current frame.The process then repeats for the next frame in the cardiac cycle.

In step 282, the translation of the LV centroid is corrected in apreferred embodiment as follows. After outlining the contour on twoadjacent frames, the contour from the earlier frame is divided into anumber of points equally spaced with respect to arc length. The contourof the later frame is then divided into a number of points equallyspaced again with respect to its arc length. Preferably, the number ofpoints of the second contour is triple the number of points of the firstcontour. For each point in the original contour, the point on the secondcontour nearest to that point is then found, and the area of thequadrilateral defined by two adjacent points on one contour and thepoints nearest to them on the next contour determines the distance thatpart of the heart wall is determined to move during the time interval.This procedure is then iterated on subsequent contours. A variant ofthis procedure may be used with certain contours whereby the points onthat contour are not re-normalized with respect to arc length; rather,the nearest points on that contour are used as the initial division whenfinding the points on the next contour (un-renormalized condition).

FIG. 2d illustrates in more detail a preferred embodiment of thealgorithm for invalidating out-of-tolerance border points implemented insteps 260, 272 and 276. As illustrated, the process starts at step 283and repeats for all rays where the border points are of the selectedtype (e.g., threshold or gradient). At step 284, it is determined if thenew border point is farther from the previous border point than thepreselected limit, and if so, the new border point is invalidated. It isthen determined at step 285 if the present frame is between enddiastolic and end systolic. If the frame is between end diastolic andend systolic, it is determined at step 286 if the previous border pointis a threshold point and if the new border point is farther outward(away from the left ventricle centroid) from the previous border pointby more than the preselected threshold border point movement limit. Ifso, the new border point is invalidated. If the same is true but theprevious border point is not a threshold point, then at step 287 the newborder point is invalidated. On the other hand, if the frame is betweenend systolic and end diastolic, it is determined at step 288 if theprevious border point is a threshold point and if the new border pointis closer to the left ventricle centroid than the previous border pointby more than the preselected threshold border point movement limit. Ifso, the new border point is invalidated. If the same is true but theprevious border point is not a threshold point, then at step 289 the newborder point is invalidated. Upon exiting steps 287 or 289, or if thepresent frame is the ED frame, the process is repeated for the next rayof the selected type.

FIG. 2e illustrates in more detail a preferred embodiment of thealgorithm for filling border gaps from the border of the previous frameas implemented in step 280. As shown, the process repeats at step 290for all gaps in the detected border, and the processing path followed isbased on whether the frames are determined at step 291 to be pre-editedor post-edited. If the frames are post-edit frames, all border points inthe gap are replaced at step 292 with the Doppler-predicted borderpoints for the present frame. In this case, the replacement of invalidborder points in step 282 (FIG. 2c) is unnecessary. On the other hand,if the frames are pre-edit frames, at step 293 the two rays on eitherside of the gap in the present frame are used to compute the distancesfrom the left ventricle centroid to the detected border points. Thiscomputation is then performed at step 294 for the two rays on eitherside of the gap in the previous frame. At step 295, the averagedifference in distance for these two rays is computed. Finally, at step296, all border points in the gap with points that are the same distancefrom the left ventricle centroid as the border points on thecorresponding rays of the previous frame (plus the average distancedifference) are replaced. At step 282 (FIG. 2c), the invalid borderpoints are then replaced with border points from the previous frame andcorrected for translation of the LV centroid. If the gap cannot befilled in the pre-edit processing step, the gap is filled in thepost-edit processing step by the Doppler-predicted boundary points.

Those skilled in the art will appreciate that the acquired boundarycontours may be used to obtain both qualitative and quantitativeinformation about the cardiac wall motion. To display the wall motion,the color quantitation of the wall motion is displayed. Colorquantitation is desirable since it provides a visible aid for thephysician to assess wall motion.

In the preferred embodiment, using the above-described edge detectionand left ventricular boundary location techniques, the endocardial wallon each video frame is outlined starting at end diastole and ending atend systole. To visualize the endocardial wall motion during the cardiaccycle, this information is displayed in accordance with the techniqueillustrated in FIG. 4. In particular, the interior portion of the leftventricle cavity corresponding to end diastole is filled with a singlecolor (such as blue) at step 300. This blue area is then transferred toeach frame of the sequence. Then, on each subsequent frame of thesequence, the left ventricle cavity on the current frame is filled withanother color such as black at step 302. Thus, if the endocardial wallis contracting from frame to frame, the following effect is observed.Namely, on the end diastolic frame, the entire left ventricle cavity isblack. On subsequent frames, the middle of the cavity is black, and anarea corresponding to the motion of the endocardial wall is filled inwith blue. This blue area thickens from frame to frame until endsystole. As shown in FIG. 5 (lower right), such color quantitationallows the viewer to easily visualize wall motion from end diastole toend systole and to ascertain the state of the contraction so that anyasymmetry may be easily observed. In addition, this excursion of thewall may be quantitatively evaluated to help determine whether the wallmotion is normal or abnormal and, if abnormal, what type of abnormalityit is.

In particular, the wall motion may be quantitatively described at step304 as follows. Assuming approximately 96 points are used for the edgedetection (although more may be used if they are found to give betterresults), the ventricle may be divided into 6 main segments, beginningat north and at 60 degree increments moving clockwise. Each main segmentis divided in half to yield 12 subsegments, with 8 samples persubsegment. The regional ejection fraction (Reg EF) for each subsegmentis then calculated as:

Reg EF=Area end diastole−area end systole Area end diastole

Generally, a Regional EF greater than 50% is normal, while a Regional EFless than 50% is abnormal. Using the calculated regional area in eachsubsegment for the end diastolic and end systolic contours,respectively, these areas are modeled as a wedge shaped region. Fromthis, the corresponding radial distance change of the end diastolic toend systolic regional contour (representing the average chord shorteningin the subsegment) is calculated. The border is then colorizedaccordingly at step 306, thus providing easily readable wall motionanalysis criteria.

In a preferred embodiment, the mean excursion of points of a particularsegment is color coded at step 306 as follows:

>3.6 mm=NORMAL (green);

1.6−3.6 mm=HYPOKINETIC (yellow);

−0.5−1.5 mm=AKINETIC (red); and

<−0.5 mm=DYSKINETIC (blue).

Graphical user interface 26 may be used to assess myocardial perfusionusing a spatial locator 22 and echocardiography images from video source12 using the workstation 10. This capability is important, for theaccurate localization of coronary artery disease and detection of smallareas of reduced myocardial blood flow are important factors in clinicalcardiac imaging. Further, the reliable localization of myocardialperfusion defects to one or more coronary arteries is of considerablepractical importance.

Moreover, tomographic imaging of myocardial perfusion offers substantialpromise for the accurate determination of the presence and extent ofcoronary artery disease by detecting smaller regions of ischemia(regions of insufficient blood flow) with improved capability foranatomic localization. Such factors have been validated with nuclearimaging techniques using thallium or sestamibi. Using a spatial locator22, echocardiographic images are digitized by video digitizer 18 andstored in host memory 30 or 32. The images are acquired at “M” degreeintervals with the 6 degrees of freedom coordinates stored with eachimage. After the injection of a contrast agent used for assessingperfusion by echocardiography (e.g., Albuminex) the images are acquiredat the same “M” degree interval and 3-D coordinates as the images takenprior to the contrast injection. The contrast may be injected at peakexercise or pharmacologic stress. By using a tomographic display of thedata, smaller areas of ischemia may be detected.

The quantitative measurements noted above may be used to assessmyocardial perfusion and other parameters indicative of the heart'sfunction. Generally, the user of the workstation 10 can view the echodirectly from the echo machine (video source) 12, from digitized imagesequences, or from videotape. Resting and stress studies may bedisplayed side-by-side so as to facilitate the detection of transientand sometimes subtle abnormalities in regional myocardial wall motion,wall thickening, and valvular function. During a conventionalinterpretation process, the physician accesses the menu items in areport generator program to evaluate normal and abnormal findings aboutthe study. The data entries are recorded in the database 24 and thenmoved to a report form in the report generator.

While using the workstation 10 of the invention, a physician viewing astudy may wish to process the digitized image sequences to improve theirquality and diagnostic value. The physician may then choose one ofseveral processing combinations from menus. The default is forprocessing average images with moderate speckle. However, the physicianmay also choose options for light or heavy speckle. After the specklereduction, the physician may want to automatically outline the cardiaccontour throughout the cardiac cycle. The physician selects this optionwhich calls the border detection algorithm described above with respectto FIGS. 2a-2 e. The physician can then select the color quantitationalgorithm as described above with respect to FIG. 4 for a revealing viewof the heart motion. A quad screen may be used to simultaneously showthe raw image data, the speckle reduced image, the edge detected image,and the color quantitated image which illustrates movement of theendocardial walls (FIG. 5). Upon completion of the study, a report istypically generated (FIG. 10).

The report generator of the invention is preferably a routine which iscalled up to permit entry of patient data for storage in database 24.The patient information is entered via a patient information screen ofthe type shown in FIG. 5 and stored in the database 24 with a uniqueidentifier for easy recall. Also, measurement data pertaining to thecurrent study is entered via a measurement screen. The type ofmeasurement is selected, e.g., 2-D, m-mode, Doppler, stress echo, ortransesophogeal echo, and then the method and heart structure areselected. The measurement data is then entered and stored in database 24in accordance with these selections. After the interpretation process bythe physician, a report is generated for preview by the physician beforethe report is printed, e-mailed or faxed.

In accordance with an automatic interpretation feature of the invention(“AutoDx”), the measurements taken during the study are automaticallycompared to the database 24 to determine if any of the measurements arehigh or low. If any measurements are outside the expected range(abnormal), that area is color-coded to indicate the abnormal valuewhich then triggers the selection of the corresponding abnormal findingwhich is automatically checked in an interpretation screen for thatstructure. The abnormal findings then are automatically listed in thefindings summary section for inclusion in the report. However, duringinterpretation, the physician is given the opportunity to select theparticular findings and to agree or disagree with the automaticanalysis. If the physician agrees with the findings, the report isgenerated automatically and previewed before printing. On the otherhand, if the physician disagrees with the findings, the physician maychange or add to the findings in the interpretation section. The updatedfindings are then printed in the report.

Preferably, database 24 further includes a reference source “EchoHelp™”that the user can utilize to assist with the interpretation process. Thereference source is a “what's this” type Help system forechocardiography that assists the physician with the interpretation byproviding descriptions of abnormalities with lists of their knowncauses. When in the interpretation screen, the physician can highlightan abnormal finding in a selected structure, check its details, and callthe EchoHelp™ feature to give a detailed differential diagnosis orexplanation of the abnormality.

In a preferred embodiment of the invention, an expert system providesautomatic interpretations of the echo-cardiograms. This expert systemuses compiled lists of echo findings by disease, and diseases by echofindings. Questions about the findings may be addressed to the expertsystem. Also, the database 24 may be searched to match the findings toknown diseases for the provision of a proposed diagnosis along with atext description of the disease and appropriate diagnosis criteria.

In accordance with the invention, the group of findings are compared todiagnostic data in the database 24 by the expert system for thedetermination of a suggested diagnosis. This suggested diagnosis andassociated descriptive text is then displayed when an “AutoDx” orautodiagnosis option is selected by the physician at the time ofinterpretation. The “AutoDx” function compares the inputted findings tothe findings of the expert system for generating a suggested diagnosis.When the “AutoDx” feature is selected during the interpretation process,the physician may select one or more of the suggested diagnoses forinclusion in the report.

For example, database 24 may contain findings for 150 or more cardiacdiseases. After findings are entered, either manually or automatically,they are compared to the database 24, where each finding has its ownnumber code of type 1, 2, or 3 as follows:

3=If patient has all of the 3's, then there is a high probability ofdiagnosis;

2=The patient must have all 2's or there cannot be a diagnosis;

1=Supporting findings; diagnosis is possible if patient has all 2's andmore than half of the 1's.

Different values for data in the respective code types may be used toautomatically identify diseases such as aortic stenosis, dilatedcardiomyopathy, and aortic aneurysms.

A sample evaluation of an echocardiogram will now be described withrespect to FIGS. 5-10.

In FIG. 5, the captured echocardiogram image is displayed in the displayarea. The technician is given the option of selecting “patient” to enterpatient data, “measurement” to perform measurements, “interpretation” tointerpret the displayed image, “AutoDx” to pull-up the suggesteddiagnosis of the expert system, and “report” to generate a report. Asillustrated in FIG. 5, the technician may enter the patient data andmake appropriate measurements for the respective structures by choosingthe “patient” and “measurement” tabs, respectively. The study and thedata entered by the technician is then stored for evaluation by thephysician. During interpretation, the physician may select the“interpretation” tab to review the study and interpret the conditions ofthe various features of the heart by selecting the appropriate heartfeature while evaluating the study. The physician may also select the“AutoDx” feature to assist with the interpretation process. Once theinterpretation is complete, the physician may then select the “report”tab for generating a report.

In particular, during interpretation, the physician selects a structureto investigate from the screen of FIG. 6. For example, in FIG. 7, thephysician has selected the left ventricle. FIG. 7 illustrates a quadpresentation of the echocardiogram image of the left ventricle, wherethe upper left corner illustrates the raw echocardiogram, while theupper right corner illustrates the speckle reduced image. The lower leftcorner illustrates the edge detection determination, while the lowerright corner illustrates the movement of the heart muscle during theheart cycle and is preferably color-coded. Any abnormalities determinedby the expert system based on the measurement data entered by thetechnician also may be displayed. From this data, the physician canidentify whether the left ventricle indeed appears to be normal orabnormal as indicated. If abnormal, the particular abnormality may beidentified and selected. The physician may agree or disagree with theindication and enter his or her diagnosis via an interpretation screenof the type indicated in FIG. 8. The results are stored in the database24 for inclusion in the report.

FIG. 9 illustrates a sample segment diagnosis “AutoDx” screen. Asdescribed above, the group of abnormal findings is automaticallycompared to the database 24 by the expert system and a suggesteddiagnosis is returned. As illustrated, the suggested diagnoses areidentified, and if selected, the corresponding diagnosis isautomatically imported into the report generator.

Finally, when the interpretation is complete, the physician selects thereport tab and previews the report stored in the database. The reportlists all abnormal findings as they are chosen, either manually, orautomatically, and illustrates what the physician has entered during theinterpretation process for inclusion in the report. When the report isready, it is printed, e-mailed, and/or faxed automatically. A sampleechocardiography report generated by the report generator of theworkstation 10 is illustrated in FIG. 10.

It will be appreciated by those skilled in the art that even thoughnumerous characteristics and advantages of the present invention havebeen set forth in the foregoing description, together with details ofthe structure and function of the invention, the disclosure isillustrative only and numerous alternate embodiments are possiblewithout departing from the novel teachings of the invention. Suchmodifications may be made in detail within the principles of theinvention to the full extent indicated by the broad general meaning ofthe terms of the appended claims.

We claim:
 1. An image processing system, comprising: an image sourcewhich digitizes and outputs a video image of a patient's anatomy; animage processor which implements at least one software process forprocessing a video image from said image source, said at least onesoftware process removing noise from said video image while enhancingthe definition of an internal structure of the patient's anatomy,automatically identifying a border of said internal structure andtracking movement of said border, and quantitating the extent of motionof at least said border of said internal structure of the patient'sanatomy during a selected time frame; and a display which displays aprocessed video image representative of the extent of motion of at leastsaid border of said internal structure of the patient's anatomy duringsaid selected time frame, wherein said at least one software processremoves noise from said video image while enhancing the definition of aninternal structure of the patient's anatomy by performing a non-lineargray scale transformation on a received video image, recursivelyaveraging the transformed video image, and performing a non-linear grayscale morphology operation on said transformed and averaged video image.2. An image processing system as in claim 1, further comprising aspatial locator that registers position and orientation of atwo-dimensional video image from said image source in athree-dimensional spatial coordinate system by overlaying athree-dimensional model of the patient's anatomy onto thetwo-dimensional video image at a corresponding position inthree-dimensional space on said display.
 3. An image processing systemas in claim 1, further comprising a graphical user interface whichpermits a viewer to perform at least one of the following functions:view video images output by said image source, select alternativeprocessing options, consult reference sources, generate reports frompull-down menus, and store, retrieve, and transmit said video images andreports including diagnostic interpretation of said video images by theviewer.
 4. An image processing system as in claim 1, wherein said atleast one software process implements said morphology operation byperforming the steps of choosing a neighborhood of each pixel to bedisplayed, selecting rays of points in N directions in said neighborhoodof said each pixel to be displayed, determining a minimum pixel valueand a maximum pixel value along each ray of points and a minimum of themaximum pixel values for said rays of points (MIN of MAX) and a maximumof said minimum pixel values for said rays of points (MAX of MIN), andreplacing said each pixel to be displayed with an average of said MIN ofMAX and MAX of MIN values.
 5. An image processing system, comprising: animage source which digitizes and outputs a video image of a patient'sheart, said image source including an image sequence synchronizer thatsynchronizes respective video image sequences of different frame lengthsof the patient's heart; an image processor which implements at least onesoftware process for processing a video image from said image source,said at least one software process removing noise from said video imagewhile enhancing the definition of a heart wall of the patient's heart,automatically identifying a border of said heart wall and trackingmovement of said heart wall, and quantitating the extent of motion of atleast said border of said heart wall of the patient's heart during aselected time frame; and a display which displays a processed videoimage representative of the extent of motion of at least said border ofsaid heart wall of the patient's heart during said selected time frame,wherein said image processor presents said respective synchronized videoimages of the patient's heart to said display for concurrent display,and wherein said respective video image sequences of the patient's heartare synchronized by a synchronization process which computes targetframe counts for systolic and diastolic portions of a cardiac cycle ofthe patient's heart, where the target systolic frames are {square rootover (3.6N)} and the target diastolic frames are N minus the targetsystolic frames, where N is the number of frames in a cardiac cycle ofthe patient's heart having a maximum number of frames in the cardiaccycle.
 6. An image processing system as in claim 5, wherein saidrespective video image sequences of the patient's heart are synchronizedand displayed in a synchronized M frame representation on said displaywhereby up to M viewing windows may be viewed concurrently.
 7. An imageprocessing system as in claim 5, wherein said synchronization processcomputes, for each cardiac cycle of each sequence of video images, anumber of systolic frames to add as the target systolic frame minus{square root over (3.6N)} (ADD SYS FRAMES), and a number of diastolicframes to add as the target diastolic frame minus (N− {square root over(3.6N)}) (ADD DIA FRAMES), where N is the number of frames in thecardiac cycle.
 8. An image processing system as in claim 7, wherein saidsynchronization process repeats the systolic frames from frame {squareroot over (3.6N)} to frame {square root over (3.6N)} minus ADD SYSFRAMES, repeats the diastolic frames for all frames in a range {squareroot over (3.6N)}+1 to N by int(ADD DIA FRAMES/{square root over(3.6N)}) times, and repeats all frames in a range M= {square root over(3.6N)}+[N− {square root over (3.6N)}−MOD(ADD DIA FRAMES)* {square rootover (3.6N)})]/2 to M+MOD(ADD DIA FRAMES, {square root over (3.6N)}) onetime each, whereby each sequence of images of a cardiac cycle has thesame number of image frames.
 9. An image processing system, comprising:an image source which digitizes and outputs a video image of a patient'sheart; an image processor which implements at least one software processfor processing a video image from said image source, said at least onesoftware process removing noise from said video image while enhancingthe definition of a heart wall of the patient's heart, automaticallyidentifying a border of said heart wall and tracking movement of saidheart wall, and quantitating the extent of motion of at least saidborder of said heart wall of the patient's heart during a selected timeframe; and a display which displays a processed video imagerepresentative of the extent of motion of at least said border of saidheart wall of the patient's heart during said selected time frame,wherein said at least one software process automatically identifies aborder of said heart wall and tracks movement of said border by taggingframes of said video image of the patient's heart corresponding toend-diastole (ED frame) and end-systole (ES frame) of a selected cardiaccycle, tracing said border of said heart wall, computing thresholds fromthe ED frame, and detecting borders of said heart wall for all frames ofsaid selected cardiac cycle using said thresholds.
 10. An imageprocessing system as in claim 9, wherein said at least one softwareprocess further comprises an expert system that evaluates data from saidvideo image of the patient's heart and suggests to a viewer variousdiagnostic possibilities that are consistent with the video image of thepatient's heart.
 11. An image processing system as in claim 9, whereinsaid at least one software process further comprises a report generatorwhich accepts diagnostic data from a viewer and generates a diagnosticreport including at least said diagnostic data.
 12. An image processingsystem as in claim 9, wherein said at least one software process furthercomprises a help system which is accessed by a viewer duringinterpretation of the video image of the patient's heart to providedescriptions of abnormalities of the heart structure with lists of knowncauses of said abnormalities.
 13. An image processing system as in claim9, wherein said at least one software process computes thresholds fromthe ED frame by setting a preset threshold, projecting N rays spaced atapproximately equal angles from a centroid of the traced heart wallborder, determining image gray-level gradient points along each of saidN rays in a vicinity of said traced heart wall border, and, if agradient point larger than said preset threshold is found along one ofsaid N rays, replacing a border point on said traced heart wall borderwith said gradient point.
 14. An image processing system as in claim 13,wherein said at least one software process further determines, for eachof M threshold values from 0 to a maximum gray level, a histogramequalized threshold value, thresholds said ED frame using the histogramequalized threshold value, performs morphological opening and closing ofsaid ED frame, finds, starting on the traced heart wall border for saidED frame, a change state point on each ray where the thresholded EDimage changes state, and, for each ray, determines an error distancebetween the change state point and the traced heart wall border.
 15. Animage processing system as in claim 14, wherein said at least onesoftware process further divides a portion of said ED frame representinga left ventricle of the patient's heart into N pie-shaped segments,identifies a selected threshold for each segment that corresponds to asmallest average error distance for that segment, compares the videoimage to the selected threshold, computes an average error distance foreach segment using the selected threshold, and, for each segment, savesthe selected threshold value as representative of said border of thepatient's heart wall if all points of the selected threshold are outsideof an existing heart wall border or if the average error distance isless than a previous average error distance multiplied by apredetermined weighting factor.
 16. An image processing system as inclaim 15, wherein said at least one software process compares an imagein each frame of said video signal in said selected cardiac cycle toselected threshold values for each segment, performs morphologicalopening and closing of each image, and invalidates border points whichare beyond said selected threshold values for the segment containingsaid border points.
 17. An image processing system as in claim 16,wherein said at least one software process finds image gray-levelgradient points along each ray in the vicinity of the border of theprevious frame of said video signal for invalidated border points andinvalidates the gradient points if a border point on a corresponding rayof the previous frame of said video signal was also determined fromimage gray-level gradient points.
 18. An image processing system as inclaim 16, wherein said at least one software process fills in gaps lessthan a predetermined number of rays in said traced border of the heartwall by interpolating border points on said traced border of the heartwall on either side of the gaps.
 19. An image processing system as inclaim 18, wherein said at least one software process fills in gapsgreater than said predetermined number of rays in said traced border ofthe heart wall by replacing border points in said gaps withDoppler-predicted border points for a present frame of said videosignal.
 20. An image processing system as in claim 18, wherein said atleast one software process fills in gaps greater than said predeterminednumber of rays in said traced border of the heart wall by computing fortwo rays on either side of a gap distances from the centroid of thetraced heart wall border to the border points on either side of said gapfor a present frame and a previous frame of said video signal, computingan average distance of said distances for said present frame and saidprevious frame, and replacing all border points in said gap with pointsthat are the same distance from the centroid of the traced heart wallborder as the border points on corresponding rays of the previous frameplus the computed average distance difference.
 21. An image processingsystem as in claim 20, wherein said at least one software processreplaces invalid border points in said traced border of the heart wallwith border points from said previous frame of said video signal aftercorrection for translation of the centroid of the traced heart wallborder from said previous frame to the present frame of said videosignal.
 22. An image processing system as in claim 21, wherein said atleast one software process translates the centroid of the traced heartwall border from said previous frame to the present frame of said videosignal by tracing the heart wall border for said previous frame and saidpresent frame of said video signal, dividing the traced heart wallborder from said previous frame into a first predetermined number ofborder points equally spaced with respect to arc length along saidtraced heart wall border, dividing the traced heart wall border of saidpresent frame into a second predetermined number of border pointsequally spaced with respect to arc length along said traced wall border,finding, for each point on the traced heart wall border of the previousframe, a replacement point on the traced heart wall border of thepresent frame which is nearest to a point on the traced heart wallborder of the previous frame, and replacing a corresponding border pointof the previous frame with the replacement point.
 23. An imageprocessing system, comprising: an image source which digitizes andoutputs a video image of a patient's heart; an image processor whichimplements at least one software process for processing a video imagefrom said image source, said at least one software process removingnoise from said video image while enhancing the definition of a heartwall of the patient's heart, automatically identifying a border of saidheart wall and tracking movement of said heart wall, and quantitatingthe extent of motion of at least said border of said heart wall of thepatient's heart during a selected time frame; and a display whichdisplays a processed video image representative of the extent of motionof at least said border of said heart wall of the patient's heart duringsaid selected time frame, wherein said at least one software processquantitates the extent of motion of at least said border of said heartwall from end-diastole to end-systole by representing frame to framemovement of said heart wall in a color which is different from abackground color of the processing video image displayed on saiddisplay.
 24. An image processing system as in claim 23, wherein said atleast one software process color codes the extent of radial movement ofradial sections of said heart wall from end-diastole to end-systole. 25.A method of imaging internal anatomy of a patient, comprising the stepsof: capturing a video image of the patient's internal anatomy; removingnoise from said video image while enhancing the definition of aninternal structure of the patient's anatomy; automatically identifying aborder of said internal structure; tracking movement of said border;quantitating the extent of motion of at least said border of saidinternal structure of the patient's anatomy during a selected timeframe; and displaying processed video images representative of theextent of motion of at least said border of said internal structure ofthe patient's anatomy during said selected time frame, wherein saidnoise removing step comprises the steps of performing a non-linear grayscale transformation on a captured video image, recursively averagingthe transformed video image, and performing a non-linear gray scalemorphology operation on said transformed and averaged video image.
 26. Amethod as in claim 25, further comprising the step of registering aposition and orientation of two-dimensional captured video images in athree-dimensional spatial coordinate system by overlaying athree-dimensional model of the patient's anatomy onto thetwo-dimensional video image at a corresponding position inthree-dimensional space for display in three dimensions.
 27. A method asin claim 26, further comprising the step of calculating a 3-D volume ofthe patient's anatomy from said two-dimensional captured video imagesand said three-dimensional model of the patient's anatomy.
 28. A methodas in claim 26, further comprising the steps of injecting a contrastagent into the patient's bloodstream after capturing the video image ofthe patient's internal anatomy at M degree intervals, capturing a videoimage of the patient's internal anatomy at said M degree intervals atpoints of injection of said contrast agent, and tomographicallydisplaying said video images before and after injection of said contrastagent.
 29. A method as in claim 25, wherein morphology operationcomprises the steps of choosing a neighborhood of each pixel to bedisplayed, selecting rays of points in N directions in said neighborhoodof said each pixel to be displayed, determining a minimum pixel valueand a maximum pixel value along each ray of points and a minimum of themaximum pixel values for said rays of points (MIN of MAX) and a maximumof said minimum pixel values for said rays of points (MAX of MIN), andreplacing said each pixel to be displayed with an average of said MIN ofMAX and MAX of MIN values.
 30. A method of imaging internal anatomy of apatient, comprising the steps of: capturing a video image of thepatient's internal anatomy; removing noise from said video image whileenhancing the definition of an internal structure of the patient'sanatomy; automatically identifying a border of said internal structure;tracking movement of said border; quantitating the extent of motion ofat least said border of said internal structure of the patient's anatomyduring a selected time frame; and displaying processed video imagesrepresentative of the extent of motion of at least said border of saidinternal structure of the patient's anatomy during said selected timeframe, wherein said video image is a video image of a patient's heartand said step of automatically identifying a border of said internalstructure comprises the steps of tagging frames of said video image ofthe patient's heart corresponding to end-diastole (ED frame) andend-systole (ES frame) of a selected cardiac cycle, tracing said borderof said heart wall, computing thresholds from the ED frame, anddetecting borders of said heart wall for all frames of said selectedcardiac cycle using said thresholds.
 31. A method as in claim 30,wherein said step of automatically identifying a border of said internalstructure comprises the steps of computing thresholds from the ED frameby setting a preset threshold, projecting N rays spaced at approximatelyequal angles from a centroid of the traced heart wall border,determining image gray-level gradient points along each of said N raysin a vicinity of said traced heart wall border, and, if a gradient pointlarger than said preset threshold is found along one of said N rays,replacing a border point on said traced heart wall border with saidgradient point.
 32. A method as in claim 31, comprising the further stepof determining, for each of M threshold values from 0 to a maximum graylevel, a histogram equalized threshold value, thresholding said ED frameusing the histogram equalized threshold value, performing morphologicalopening and closing of said ED frame, finding, starting on the tracedheart wall border for said ED frame, a change state point on each raywhere the thresholded ED image changes state, and, for each ray,determining an error distance between the change state point and thetraced heart wall border.
 33. A method as in claim 32, comprising thefurther steps of dividing a portion of said ED frame representing a leftventricle of the patient's heart into N pie-shaped segments, identifyinga selected threshold for each segment that corresponds to a smallestaverage error distance for that segment, comparing the video image tothe selected threshold, computing an average error distance for eachsegment using the selected threshold, and, for each segment, saving theselected threshold value as representative of said border of thepatient's heart wall if all points of the selected threshold are outsideof an existing heart wall border or if the average error distance isless than a previous average error distance multiplied by apredetermined weighting factor.
 34. A method as in claim 33, comprisingthe further steps of comparing an image in each frame of said videosignal in said selected cardiac cycle to selected threshold values foreach segment, performing morphological opening and closing of eachimage, and invalidating border points which are beyond said selectedthreshold values for the segment containing said border points.
 35. Amethod as in claim 34, comprising the further steps of finding imagegray-level gradient points along each ray in the vicinity of the borderof the previous frame of said video signal for invalidated border pointsand invalidating the gradient points if a border point on acorresponding ray of the previous frame of said video signal was alsodetermined from image gray-level gradient points.
 36. A method as inclaim 34, comprising the additional step of filling in gaps less than apredetermined number of rays in said traced border of the heart wall byinterpolating border points on said traced border of the heart wall oneither side of the gaps.
 37. A method as in claim 36, comprising theadditional step of filling in gaps greater than said predeterminednumber of rays in said traced border of the heart wall by replacingborder points in said gaps with Doppler-predicted border points for apresent frame of said video signal.
 38. A method as in claim 36,comprising the additional step of filling in gaps greater than saidpredetermined number of rays in said traced border of the heart wall bycomputing for two rays on either side of a gap distances from thecentroid of the traced heart wall border to the border points on eitherside of said gap for a present frame and a previous frame of said videosignal, computing an average distance of said distances for said presentframe and said previous frame, and replacing all border points in saidgap with points that are the same distance from the centroid of thetraced heart wall border as the border points on corresponding rays ofthe previous frame plus the computed average distance difference.
 39. Amethod as in claim 38, wherein said step of replacing invalid borderpoints comprises the step of replacing invalid border points in saidtraced border of the heart wall with border points from said previousframe of said video signal after correcting for translation of thecentroid of the traced heart wall border from said previous frame to thepresent frame of said video signal.
 40. A method in claim 39, whereinsaid step of translating the centroid of the traced heart wall borderfrom said previous frame to the present frame of said video signalcomprises the steps of tracing the heart wall border for said previousframe and said present frame of said video signal, dividing the tracedheart wall border from said previous frame into a first predeterminednumber of border points equally spaced with respect to arc length alongsaid traced heart wall border, dividing the traced heart wall border ofsaid present frame into a second predetermined number of border pointsequally spaced with respect to arc length along said traced wall border,finding, for each point on the traced heart wall border of the previousframe, a replacement point on the traced heart wall border of thepresent frame which is nearest to a point on the traced heart wallborder of the previous frame, and replacing a corresponding border pointof the previous frame with the replacement point.
 41. A method ofimaging internal anatomy of a patient, comprising the steps of:capturing a video image of the patient's internal anatomy; removingnoise from said video image while enhancing the definition of aninternal structure of the patient's anatomy; automatically identifying aborder of said internal structure; tracking movement of said border;quantitating the extent of motion of at least said border of saidinternal structure of the patient's anatomy during a selected timeframe; and displaying processed video images representative of theextent of motion of at least said border of said internal structure ofthe patient's anatomy during said selected time frame, wherein saidvideo image is a video image of a patient's heart, and said step ofquantitating the extent of motion of at least said border of said heartwall from end-diastole to end-systole during said selected time framecomprising the step of quantitating the extent of motion of at leastsaid border by representing frame to frame movement of said heart wallin a color which is different from a background color of the processingvideo image displayed on said display.
 42. A method as in claim 41,wherein said quantitating step comprises the additional step of colorcoding the extent of radial movement of radial sections of said heartwall from end-diastole to end-systole.
 43. A method of imaging apatient's heart, comprising the steps of: capturing respective videoimages of a patient's heart; removing noise from said video images whileenhancing the definition of an internal structure of the patient'sheart; automatically identifying a border of said heart; trackingmovement of said border; quantitating the extent of motion of at leastsaid border of said heart during a selected time frame; synchronizingsaid respective video images of the patient's heart; and displaying saidsynchronized images in a synchronized M frame representation whereby upto M viewing windows may be viewed concurrently, wherein saidsynchronization step comprises the step of computing target frame countsfor systolic and diastolic portions of a cardiac cycle of the patient'sheart, where the target systolic frames are {square root over (3.6N)}and the target diastolic frames are N minus the target systolic frames,where N is the number of frames in a cardiac cycle of the patient'sheart having a maximum number of frames in the cardiac cycle.
 44. Amethod in claim 43, wherein said synchronization step comprises theadditional step of computing, for each cardiac cycle of each sequence ofvideo images, a number of systolic frames to add as the target systolicframe minus {square root over (3.6N)} (ADD SYS FRAMES), and a number ofdiastolic frames to add as the target diastolic frame minus (N− {squareroot over (3.6N)}) (ADD DIA FRAMES), where N is the number of frames inthe cardiac cycle.
 45. A method as in claim 44, wherein saidsynchronization step comprises the steps of repeating the systolicframes from frame {square root over (3.6N)} to frame {square root over(3.6N)} minus ADD SYS FRAMES, repeating the diastolic frames for allframes in a range {square root over (3.6N)}+1 to N by int(ADD DIAFRAMES/{square root over (3.6N)}) times, and repeating all frames in arange M= {square root over (3.6N)}+[N− {square root over (3.6N)}−MOD(ADDDIA FRAMES)*{square root over (3.6N)}]/2 to M+MOD(ADD DIA FRAMES,{square root over (3.6N)}) one time each, whereby each sequence ofimages of a cardiac cycle has the same number of image frames.
 46. Amethod as in claim 43, comprises the additional steps of acceptingdiagnostic data from a viewer and generating a diagnostic reportincluding at least said diagnostic data.